Investigation of Mechanical and Physical Properties of PET Nanofiber Hollow Yarn
Bibliographic record
Abstract
In this study, mechanical and physical properties of polyethylene terephthalate (PET) nanofiber hollow yarn in various concentrations of PET polymeric solution were investigated. First, five different concentrations (18, 21, 24, 27, and 30% w/v) of PET solutions were prepared in (TFA) / (DCM) mixtures (70: 30 v/v), and then the electrospining was done for each concentration in a way that to put PVA multifilament in the core and to twist PET nanofibers onto multifilament yarn as a sheath simultaneously, followed by dissolving PVA yarn in hot water, PET nanofiber hollow yarn was produce. The surveys of mechanical properties of PVA multifilament yarn, core-sheath yarns and hollow yarns showed that the PVA multifilament yarn and nanofiber hollow yarn in concentrations of 30% w/v PET polymeric solution had the highest and lowest average strength respectively. Also surveys of elongation showed that PVA multifilament yarn, core-sheath yarns and hollow yarns did not have any significant difference on average extension. Regarding physical properties, the wicking property of hollow yarns in different concentrations of polymeric solution were surveyed. Results showed that the PET nanofiber hollow yarn in concentration of 24% w/v PET polymeric solution had the highest coefficient wicking. More the regain moisture of hollow yarns were surveyed and concluded that the PET nanofiber hollow yarn in concentrations of 18% w/v PET polymeric solution had the most regain moisture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".